Many state highway agencies are in the process of implementing the AASHTOWare Pavement ME Design (PMED) software for routine pavement design. However, a recurring implementation challenge has been the need to locally calibrate the software to reflect an agency’s design and construction practices, materials, and climate. This study introduced a framework to automate the calibration processes of the PMED performance models. This automated technique can search PMED output files and identify relevant damages/distresses for a project on a particular date. After obtaining this damage/distress information, the technique conducts model verification with the global calibration factors. Transfer function coefficients are then automatically derived following an optimization technique and numerical measures of goodness-of-fit. An equivalence statistical testing approach is conducted to ensure predicted performance results are in agreement with the measured data. The automated technique allows users to select one of three sampling approaches: split sampling, jackknifing, or bootstrapping. Based on the sampling approach chosen, the automated technique provides the calibration coefficients or suitable ranges for the coefficients and shows the results graphically. Model bias, standard error, sum squared error, and p-value from the paired t-test are also reported to assess efficacy of the calibration process.

许多州高速公路局正在为常规路面设计实施AASHTOWare路面ME设计（PMED）软件。但是，反复出现的实施挑战是需要对软件进行本地校准，以反映代理商的设计和施工实践，材料和气候。这项研究引入了一个框架来自动执行PMED性能模型的校准过程。这项自动化技术可以搜索PMED输出文件，并在特定日期识别项目的相关损害/困境。获得此损坏/遇险信息后，该技术将使用全局校准因子进行模型验证。然后，根据优化技术和拟合优度的数值度量，自动得出传递函数系数。进行了等效的统计测试方法，以确保预测的性能结果与测量数据一致。自动化技术允许用户选择以下三种采样方法之一：分割采样，顶进或自举。基于所选的采样方法，自动化技术可提供校准系数或系数的合适范围，并以图形方式显示结果。模型偏差，标准误差，平方和误差和 自动化技术可提供校准系数或系数的合适范围，并以图形方式显示结果。模型偏差，标准误差，平方和误差和 自动化技术可提供校准系数或系数的合适范围，并以图形方式显示结果。模型偏差，标准误差，平方和误差和还报告了配对t检验的p值，以评估校准过程的功效。